In the past two years, several applications of generating three-dimensional (3D) buildings from OpenStreetMap (OSM) have been made available, for instance, OSM-3D, OSM2World, OSM Building, etc. In these projects, 3D buildings are reconstructed using the buildings’ footprints and information about their attributes, which are documented as tags in OSM. Therefore, the quality of 3D buildings relies strongly on the quality of the building footprints data in OSM. This article is dedicated to a quality assessment of building footprints data in OSM for the German city of Munich, which is one of the most developed cities in OSM. The data are evaluated in terms of completeness, semantic accuracy, position accuracy, and shape accuracy by using building footprints in ATKIS (German Authority Topographic–Cartographic Information System) as reference data. The process contains three steps: finding correspondence between OSM and ATKIS data, calculating parameters of the four quality criteria, and statistical analysis. The results show that OSM footprint data in Munich have a high completeness and semantic accuracy. There is an offset of about four meters on average in terms of position accuracy. With respect to shape, OSM building footprints have a high similarity to those in ATKIS data. However, some architectural details are missing; hence, the OSM footprints can be regarded as a simplified version of those in ATKIS data.

In recent years, social media emerged as a potential resource to improve the management of crisis situations such as disasters triggered by natural hazards. Although there is a growing research body concerned with the analysis of the usage of social media during disasters, most previous work has concentrated on using social media as a stand-alone information source, whereas its combination with other information sources holds a still underexplored potential. This article presents an approach to enhance the identification of relevant messages from social media that relies upon the relations between georeferenced social media messages as Volunteered Geographic Information and geographic features of flood phenomena as derived from authoritative data (sensor data, hydrological data and digital elevation models). We apply this approach to examine the micro-blogging text messages of the Twitter platform (tweets) produced during the River Elbe Flood of June 2013 in Germany. This is performed by means of a statistical analysis aimed at identifying general spatial patterns in the occurrence of flood-related tweets that may be associated with proximity to and severity of flood events. The results show that messages near (up to 10 km) to severely flooded areas have a much higher probability of being related to floods. In this manner, we conclude that the geographic approach proposed here provides a reliable quantitative indicator of the usefulness of messages from social media by leveraging the existing knowledge about natural hazards such as floods, thus being valuable for disaster management in both crisis response and preventive monitoring.

The OpenStreetMap (OSM) project is a prime example in the field of Volunteered Geographic Information (VGI). Worldwide, several hundred thousand people are currently contributing information to the “free” geodatabase. However, the data contributions show a geographically heterogeneous pattern around the globe. Germany counts as one of the most active countries in OSM; thus, the German street network has undergone an extensive development in recent years. The question that remains is this: How does the street network perform in a relative comparison with a commercial dataset? By means of a variety of studies, we show that the difference between the OSM street network for car navigation in Germany and a comparable proprietary dataset was only 9% in June 2011. The results of our analysis regarding the entire street network showed that OSM even exceeds the information provided by the proprietary dataset by 27%. Further analyses show on what scale errors can be reckoned with in the topology of the street network, and the completeness of turn restrictions and street name information. In addition to the analyses conducted over the past few years, projections have additionally been made about the point in time by which the OSM dataset for Germany can be considered “complete” in relative comparison to a commercial dataset.

OpenStreetMap (OSM) has evolved as a popular dataset for global urban analyses, such as assessing progress towards the Sustainable Development Goals. However, many analyses do not account for the uneven spatial coverage of existing data. We employ a machine-learning model to infer the completeness of OSM building stock data for 13,189 urban agglomerations worldwide. For 1,848 urban centres (16% of the urban population), OSM building footprint data exceeds 80% completeness, but completeness remains lower than 20% for 9,163 cities (48% of the urban population). Although OSM data inequalities have recently receded, partially as a result of humanitarian mapping efforts, a complex unequal pattern of spatial biases remains, which vary across various human development index groups, population sizes and geographic regions. Based on these results, we provide recommendations for data producers and urban analysts to manage the uneven coverage of OSM data, as well as a framework to support the assessment of completeness biases.

The OpenStreetMap (OSM) project, founded in 2004, has gathered an exceptional amount of interest in recent years and counts as one of the most impressive sources of Volunteered Geographic Information (VGI) on the Internet. In total, more than half a million members had registered for the project by the end of 2011. However, while this number of contributors seems impressive, questions remain about the individual contributions that have been made by the project members. This research article contains several studies regarding the contributions by the community of the project. The results show that only 38% (192,000) of the registered members carried out at least one edit in the OSM database and that only 5% (24,000) of all members actively contributed to the project in a more productive way. The majority of the members are located in Europe (72%) and each member has an activity area whose size may range from one soccer field up to more than 50 km². In addition to several more analyses conducted for this article, predictions will be made about how this newly acquired knowledge can be used for future research.